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HFEPX Archive Slice

HFEPX Weekly Archive: 2025-W42

Updated from current HFEPX corpus (Apr 12, 2026). 72 papers are grouped in this daily page.

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Updated from current HFEPX corpus (Apr 12, 2026). 72 papers are grouped in this daily page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequently cited benchmark: APPS. Common metric signal: accuracy. Use this page to compare protocol setup, judge behavior, and labeling design decisions before running new eval experiments. Newest paper in this set is from Oct 19, 2025.

Papers: 72 Last published: Oct 19, 2025 Global RSS

Researcher Quick Triage

Use this archive page for time-slice monitoring (what changed in evaluation methods, metrics, and protocol quality this period). Quality band: High .

Analysis blocks are computed from the loaded sample (60 of 72 papers).

High-Signal Coverage

100.0%

60 / 60 papers are not low-signal flagged.

Benchmark Anchors

10.0%

Papers with benchmark/dataset mentions in extraction output.

Metric Anchors

35.0%

Papers with reported metric mentions in extraction output.

  • 0 papers report explicit quality controls for this archive period.
  • Prioritize papers with both benchmark and metric anchors for reliable longitudinal comparisons.

Primary action: Use this slice for trend comparison: review top papers first, then validate shifts in the protocol matrix.

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Why This Time Slice Matters

  • 11.1% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 29.2% of papers in this hub.
  • APPS is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways For This Period

  • Quality-control reporting is sparse in this slice; prioritize papers with explicit calibration or adjudication steps.
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.

Start Here (Highest-Signal Papers In This Slice)

Ranked by protocol completeness and evidence density for faster period-over-period review.

Protocol Matrix (Top 10)

Quickly compare method ingredients across this archive slice.

Paper Eval Modes Benchmarks Metrics Quality Controls
When to Ensemble: Identifying Token-Level Points for Stable and Fast LLM Ensembling

Oct 17, 2025

Automatic Metrics MATH 500, BBH Accuracy Not reported
LLM Prompt Duel Optimizer: Efficient Label-Free Prompt Optimization

Oct 14, 2025

Not reported BIG Bench, BBH Cost Not reported
Readers Prefer Outputs of AI Trained on Copyrighted Books over Expert Human Writers

Oct 15, 2025

Automatic Metrics Not reported Cost Not reported
CoGate-LSTM: Prototype-Guided Feature-Space Gating for Mitigating Gradient Dilution in Imbalanced Toxic Comment Classification

Oct 19, 2025

Automatic Metrics Not reported Accuracy, F1 Not reported
FrugalPrompt: Reducing Contextual Overhead in Large Language Models via Token Attribution

Oct 18, 2025

Automatic Metrics Not reported Latency Not reported
ScholarEval: Research Idea Evaluation Grounded in Literature

Oct 17, 2025

Not reported Scholareval Not reported Not reported
BIOGEN: Evidence-Grounded Multi-Agent Reasoning Framework for Transcriptomic Interpretation in Antimicrobial Resistance

Oct 17, 2025

Automatic Metrics Not reported Bertscore, Hallucination rate Not reported
HypoSpace: Evaluating LLM Creativity as Set-Valued Hypothesis Generators under Underdetermination

Oct 17, 2025

Automatic Metrics Not reported Precision Not reported
MNO: Multiscale Neural Operator for 3D Computational Fluid Dynamics

Oct 17, 2025

Automatic Metrics Not reported Accuracy Not reported
AI-BAAM: AI-Driven Bank Statement Analytics as Alternative Data for Malaysian MSME Credit Scoring

Oct 17, 2025

Automatic Metrics Not reported Auroc Not reported
Researcher Workflow (Detailed)

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (11.1% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (0% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (9.7% vs 35% target).

  • Gap: Papers naming evaluation metrics

    Coverage is a replication risk (19.4% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (6.9% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (16.7% vs 35% target).

Strengths

  • This hub still surfaces a concentrated paper set for protocol triage and replication planning.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (6.9% coverage).
  • Annotation unit is under-specified (16.7% coverage).

Suggested Next Analyses

  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.
  • Stratify by benchmark (APPS vs BBH) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and coherence.

Recommended Queries

Known Limitations
  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (6.9% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Research Utility Snapshot (Detailed)

Evaluation Modes

  • Automatic Metrics (21)
  • Simulation Env (4)
  • Human Eval (2)

Top Metrics

  • Accuracy (5)
  • Coherence (3)
  • Cost (3)
  • F1 (2)

Top Benchmarks

  • APPS (1)
  • BBH (1)
  • BIG Bench (1)
  • GPQA (1)

Quality Controls

Papers In This Archive Slice

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